warped_ground_truth_image = ground_truth_images.warp_to(reference_frame.mask, tr) warped_intensity_image = MaskedNDImage(warped_intensity_image.pixels, mask=warped_intensity_image.mask) warped_intensity_image.view() # <codecell> # Use ground truth light estimate_light = lights[sfs_index, :] print estimate_light # <codecell> from photometric_stereo import photometric_stereo as ps ground_truth_normals, ground_truth_albedo = ps(warped_ground_truth_image, lights) # <codecell> from pybug.image import MaskedNDImage from scipy.ndimage.filters import gaussian_filter, median_filter from geometric_sfs import geometric_sfs as sfs, worthington_hancock_sfs from mapping import AEP, PGA, Spherical, ImageMapper, IdentityMapper # mapping_object = ImageMapper(PGA(intrinsic_mean_normals.as_vector(keep_channels=True))) mapping_object = ImageMapper(Spherical()) warped_intensity_image = MaskedNDImage(warped_intensity_image.pixels.copy(), mask=warped_intensity_image.mask) mean_normals_image = warped_intensity_image.from_vector(mean_normals, n_channels=3) # Normalise the image so that it has unit albedo? warped_intensity_image.masked_pixels /= ground_truth_albedo.masked_pixels
'PTS', ibug_68_closed_mouth)
# Constrain to mask
ground_truth_images.constrain_mask_to_landmarks(
group='ibug_68_closed_mouth', label='all')
intensity_image.constrain_mask_to_landmarks(
group='ibug_68_closed_mouth', label='all')
intensity_image.crop_to_landmarks(group='ibug_68_closed_mouth',
label='all', boundary=2)
ground_truth_images.crop_to_landmarks(group='ibug_68_closed_mouth',
label='all', boundary=2)
temp_texture = subject_images[sfs_index]
# Perform Photometric Stereo
ground_truth_normals, ground_truth_albedo = ps(ground_truth_images, lights)
ground_truth_depth = frankotchellappa(-ground_truth_normals.pixels[:, :, 0],
ground_truth_normals.pixels[:, :, 1])
ground_truth_depth_image = DepthImage((ground_truth_depth - np.min(ground_truth_depth)) / 2,
texture=temp_texture)
normals[subject_id]['ground_truth'] = ground_truth_normals
# TODO: save images
#ground_truth_depth_image.view(mode='mesh')
#save_result_images(subject_id, 'all', 'groundtruth')
for k, feature_space in enumerate(feature_spaces):
print "Running {0} for {1}".format(feature_space, subject_id)
model_path = '/vol/atlas/pts08/cvpr/frgc_spring2003_sfs_tps_{0}_{1}.pkl'.format(subject_id, feature_space)
with open(model_path, 'rb') as f:
model = cPickle.load(f)
warped_intensity_image = MaskedNDImage(warped_intensity_image.pixels,
mask=warped_intensity_image.mask)
warped_intensity_image.view()
# <codecell>
# Use ground truth light
estimate_light = lights[sfs_index, :]
print estimate_light
# <codecell>
from photometric_stereo import photometric_stereo as ps
ground_truth_normals, ground_truth_albedo = ps(warped_ground_truth_image,
lights)
# <codecell>
from pybug.image import MaskedNDImage
from scipy.ndimage.filters import gaussian_filter, median_filter
from geometric_sfs import geometric_sfs as sfs, worthington_hancock_sfs
from mapping import AEP, PGA, Spherical, ImageMapper, IdentityMapper
# mapping_object = ImageMapper(PGA(intrinsic_mean_normals.as_vector(keep_channels=True)))
mapping_object = ImageMapper(Spherical())
warped_intensity_image = MaskedNDImage(warped_intensity_image.pixels.copy(),
mask=warped_intensity_image.mask)
mean_normals_image = warped_intensity_image.from_vector(mean_normals,
n_channels=3)
# Normalise the image so that it has unit albedo?
import numpy as np
import cv2
import os
from utils import *
from estimate_alb_nrm import estimate_alb_nrm
from check_integrability import check_integrability
from construct_surface import construct_surface
from photometric_stereo import photometric_stereo as ps
file = './photometrics_images/MonkeyGray'
nbr_images = [5, 25, 45, 65, 85, 105, 121]
for n in nbr_images:
ps(file, n, shadow_trick=False)
ground_truth_images.landmarks['PTS'] = intensity_image.landmarks['PTS']
# Label with correct labels
labeller([ground_truth_images, intensity_image],
'PTS', ibug_68_closed_mouth)
# Constrain to mask
ground_truth_images.constrain_mask_to_landmarks(
group='ibug_68_closed_mouth', label='all')
intensity_image.constrain_mask_to_landmarks(
group='ibug_68_closed_mouth', label='all')
temp_texture = RGBImage(np.dstack([subject_images[0].pixels] * 3))
# Perform Photometric Stereo
ground_truth_normals, ground_truth_albedo = ps(ground_truth_images, lights)
ground_truth_depth = frankotchellappa(ground_truth_normals.pixels[:, :, 0],
ground_truth_normals.pixels[:, :, 1])
ground_truth_depth_image = DepthImage((ground_truth_depth - np.min(ground_truth_depth)),
texture=temp_texture,
mask=intensity_image.mask)
normals[subject_id]['ground_truth'] = ground_truth_normals
# TODO: save images
#ground_truth_depth_image.view(mode='mesh')
#save_result_images(subject_id, 'all', 'groundtruth')
for k, feature_space in enumerate(feature_spaces):
print "Running {0} for {1}".format(feature_space, subject_id)
model_path = '/vol/atlas/pts08/cvpr/frgc_spring2003_sfs_tps_{0}_{1}.pkl'.format(subject_id, feature_space)
with open(model_path, 'rb') as f:
